To mitigate the deficiencies of existing measures, we propose to explore the group effect by leveraging the social identity theory (\({\mathsf {SIT}}\) ), which is a fundamental concept in social psychology and is widely applied in domains of human health [1]}, team sports [2]}, computer-supported cooperative works [3]} and fake news detection [4]}.
\({\mathsf {SIT}}\)  describes that a target \(v_t\)  tends to endorse attitudes and behaviors of groups of users, who are assessed as us in \(v_t\) 's cognition.
In other words, given a target \(v_t\)  and an identified group \(\mathcal {C} \)  of \(v_t\) , \(v_t\)  is more likely to adopt the invitation from the source \(v_s\in \mathcal {C} \) . Unfortunately, the identification process is difficult to be quantified due to perceptual factors.
To this end, we seamlessly reify the factors of \({\mathsf {SIT}}\)  into \({\mathtt {TFC}}\)  measures, which consider a group's local and global information. For example, by modeling the social network as a physical system, we propose to measure the attractive spring-like force related to a group, in which the information of group connectivity and in-group user homogeneity are naturally incorporated.
In contrast, previous \({\mathtt {TFC}}\)  measures only consider similarities of group memberships [5]}, [6]}.
